Abstract

IntroductionGenomic base composition ranges from less than 25% AT to more than 85% AT in prokaryotes. Since only a small fraction of prokaryotic genomes is not protein coding even a minor change in genomic base composition will induce profound protein changes. We examined how amino acid and codon frequencies were distributed in over 2000 microbial genomes and how these distributions were affected by base compositional changes. In addition, we wanted to know how genome-wide amino acid usage was biased in the different genomes and how changes to base composition and mutations affected this bias. To carry this out, we used a Generalized Additive Mixed-effects Model (GAMM) to explore non-linear associations and strong data dependences in closely related microbes; principal component analysis (PCA) was used to examine genomic amino acid- and codon frequencies, while the concept of relative entropy was used to analyze genomic mutation rates.ResultsWe found that genomic amino acid frequencies carried a stronger phylogenetic signal than codon frequencies, but that this signal was weak compared to that of genomic %AT. Further, in contrast to codon usage bias (CUB), amino acid usage bias (AAUB) was differently distributed in AT- and GC-rich genomes in the sense that AT-rich genomes did not prefer specific amino acids over others to the same extent as GC-rich genomes. AAUB was also associated with relative entropy; genomes with low AAUB contained more random mutations as a consequence of relaxed purifying selection than genomes with higher AAUB.ConclusionGenomic base composition has a substantial effect on both amino acid- and codon frequencies in bacterial genomes. While phylogeny influenced amino acid usage more in GC-rich genomes, AT-content was driving amino acid usage in AT-rich genomes. We found the GAMM model to be an excellent tool to analyze the genomic data used in this study.

Highlights

  • Genomic base composition ranges from less than 25% AT to more than 85% AT in prokaryotes

  • In contrast to codon usage bias (CUB), amino acid usage bias (AAUB) was differently distributed in AT- and GC-rich genomes in the sense that AT-rich genomes did not prefer specific amino acids over others to the same extent as GC-rich genomes

  • We found that amino acid usage was strongly associated with genomic %AT but that phylogeny appeared to exert a stronger influence in GC-rich microbes

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Summary

Introduction

Genomic base composition ranges from less than 25% AT to more than 85% AT in prokaryotes. We wanted to know how genome-wide amino acid usage was biased in the different genomes and how changes to base composition and mutations affected this bias To carry this out, we used a Generalized Additive Mixed-effects Model (GAMM) to explore non-linear associations and strong data dependences in closely related microbes; principal component analysis (PCA) was used to examine genomic amino acid- and codon frequencies, while the concept of relative entropy was used to analyze genomic mutation rates. We discuss possible influences on amino acid- and codon-usage from purifying selection, random mutations and selective pressures in general using the concept of relative entropy [11] This was carried out by first downloading 2032 microbial genomes from GenBank (See Table S1) (http://www.ncbi.nlm.nih.gov/genome/) and analyzing both amino acid- and codon frequencies using principal component analysis (PCA). We applied a Generalized Additive Mixed-effects Model (GAMM) [14,15] to analyze explanatory variables such as genomic %AT, genome size, relative entropy, AAUB and CUB, many of which exhibiting non-linear trends as well as hierarchical structures of dependency ranging from strong within species to weak within phyla

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